
"""simple docstring"""



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 			_UpperCamelCase						(    _A       ,   _A=0.999       ,   _A="cosine"       ,   )     ->	int:





    """simple docstring"""

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_A   ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_A   ):
            return math.exp(t * -12.0   )

    else:
        raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

    _UpperCAmelCase        = []
    for i in range(_A   ):
        _UpperCAmelCase        = i / num_diffusion_timesteps
        _UpperCAmelCase        = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(_A   ) / alpha_bar_fn(_A   )       ,   _A   )   )
    return torch.tensor(_A       ,   dtype=torch.floataa   )







class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase						):
    a   :					Dict			     =					[e.name for e in KarrasDiffusionSchedulers]
    a   :					str			     =					2







    @register_to_config
    def __init__(      self      :							List[Any]  ,       __UpperCamelCase      :							int = 10_00  ,       __UpperCamelCase      :							float = 0.0_0_0_8_5  ,       __UpperCamelCase      :							float = 0.0_1_2  ,       __UpperCamelCase      :							str = "linear"  ,       __UpperCamelCase      :							Optional[Union[np.ndarray, List[float]]] = None  ,       __UpperCamelCase      :							str = "epsilon"  ,       __UpperCamelCase      :							Optional[bool] = False  ,       __UpperCamelCase      :							Optional[bool] = False  ,       __UpperCamelCase      :							float = 1.0  ,       __UpperCamelCase      :							str = "linspace"  ,       __UpperCamelCase      :							int = 0  ,       )		->Optional[int]:






        '''simple docstring'''






        if trained_betas is not None:
            _UpperCAmelCase        = torch.tensor(__UpperCamelCase  ,       dtype=torch.floataa  )
        elif beta_schedule == "linear":
            _UpperCAmelCase        = torch.linspace(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            _UpperCAmelCase        = (
                torch.linspace(beta_start**0.5  ,       beta_end**0.5  ,       __UpperCamelCase  ,       dtype=torch.floataa  ) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            _UpperCAmelCase        = betas_for_alpha_bar(__UpperCamelCase  ,       alpha_transform_type="""cosine"""  )
        elif beta_schedule == "exp":
            _UpperCAmelCase        = betas_for_alpha_bar(__UpperCamelCase  ,       alpha_transform_type="""exp"""  )
        else:
            raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""  )

        _UpperCAmelCase        = 1.0 - self.betas
        _UpperCAmelCase        = torch.cumprod(self.alphas  ,       dim=0  )

        #  set all values
        self.set_timesteps(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )
        _UpperCAmelCase        = use_karras_sigmas







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Union[str, Any]=None  )		->Optional[int]:






        '''simple docstring'''






        if schedule_timesteps is None:
            _UpperCAmelCase        = self.timesteps

        _UpperCAmelCase        = (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter  ) == 0:
            _UpperCAmelCase        = 1 if len(__UpperCamelCase  ) > 1 else 0
        else:
            _UpperCAmelCase        = timestep.cpu().item() if torch.is_tensor(__UpperCamelCase  ) else timestep
            _UpperCAmelCase        = self._index_counter[timestep_int]

        return indices[pos].item()







    @property
    def   _snake_case       (      self      :							Tuple  )		->str:






        '''simple docstring'''






        if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5







    def   _snake_case       (      self      :							Optional[int]  ,       __UpperCamelCase      :							torch.FloatTensor  ,       __UpperCamelCase      :							Union[float, torch.FloatTensor]  ,       )		->torch.FloatTensor:






        '''simple docstring'''






        _UpperCAmelCase        = self.index_for_timestep(__UpperCamelCase  )

        _UpperCAmelCase        = self.sigmas[step_index]
        _UpperCAmelCase        = sample / ((sigma**2 + 1) ** 0.5)
        return sample







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Union[str, torch.device] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = num_inference_steps

        _UpperCAmelCase        = num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
            _UpperCAmelCase        = np.linspace(0  ,       num_train_timesteps - 1  ,       __UpperCamelCase  ,       dtype=__UpperCamelCase  )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
            _UpperCAmelCase        = num_train_timesteps // self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            _UpperCAmelCase        = (np.arange(0  ,       __UpperCamelCase  ) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase  )
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            _UpperCAmelCase        = num_train_timesteps / self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            _UpperCAmelCase        = (np.arange(__UpperCamelCase  ,       0  ,       -step_ratio  )).round().copy().astype(__UpperCamelCase  )
            timesteps -= 1
        else:
            raise ValueError(
                f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""  )

        _UpperCAmelCase        = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        _UpperCAmelCase        = np.log(__UpperCamelCase  )
        _UpperCAmelCase        = np.interp(__UpperCamelCase  ,       np.arange(0  ,       len(__UpperCamelCase  )  )  ,       __UpperCamelCase  )

        if self.config.use_karras_sigmas:
            _UpperCAmelCase        = self._convert_to_karras(in_sigmas=__UpperCamelCase  ,       num_inference_steps=self.num_inference_steps  )
            _UpperCAmelCase        = np.array([self._sigma_to_t(__UpperCamelCase  ,       __UpperCamelCase  ) for sigma in sigmas]  )

        _UpperCAmelCase        = np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
        _UpperCAmelCase        = torch.from_numpy(__UpperCamelCase  ).to(device=__UpperCamelCase  )
        _UpperCAmelCase        = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2  ), sigmas[-1:]]  )

        _UpperCAmelCase        = torch.from_numpy(__UpperCamelCase  )
        _UpperCAmelCase        = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2  )]  )

        if str(__UpperCamelCase  ).startswith("""mps"""  ):
            # mps does not support float64
            _UpperCAmelCase        = timesteps.to(__UpperCamelCase  ,       dtype=torch.floataa  )
        else:
            _UpperCAmelCase        = timesteps.to(device=__UpperCamelCase  )

        # empty dt and derivative
        _UpperCAmelCase        = None
        _UpperCAmelCase        = None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        _UpperCAmelCase        = defaultdict(__UpperCamelCase  )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = np.log(__UpperCamelCase  )

        # get distribution
        _UpperCAmelCase        = log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        _UpperCAmelCase        = np.cumsum((dists >= 0)  ,       axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
        _UpperCAmelCase        = low_idx + 1

        _UpperCAmelCase        = log_sigmas[low_idx]
        _UpperCAmelCase        = log_sigmas[high_idx]

        # interpolate sigmas
        _UpperCAmelCase        = (low - log_sigma) / (low - high)
        _UpperCAmelCase        = np.clip(__UpperCamelCase  ,       0  ,       1  )

        # transform interpolation to time range
        _UpperCAmelCase        = (1 - w) * low_idx + w * high_idx
        _UpperCAmelCase        = t.reshape(sigma.shape  )
        return t







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							torch.FloatTensor  ,       __UpperCamelCase      :							int  )		->torch.FloatTensor:






        '''simple docstring'''






        _UpperCAmelCase        = in_sigmas[-1].item()
        _UpperCAmelCase        = in_sigmas[0].item()

        _UpperCAmelCase        = 7.0  # 7.0 is the value used in the paper
        _UpperCAmelCase        = np.linspace(0  ,       1  ,       __UpperCamelCase  )
        _UpperCAmelCase        = sigma_min ** (1 / rho)
        _UpperCAmelCase        = sigma_max ** (1 / rho)
        _UpperCAmelCase        = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Union[str, Any]:






        '''simple docstring'''






        return self.dt is None







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Union[torch.FloatTensor, np.ndarray]  ,       __UpperCamelCase      :							Union[float, torch.FloatTensor]  ,       __UpperCamelCase      :							Union[torch.FloatTensor, np.ndarray]  ,       __UpperCamelCase      :							bool = True  ,       )		->Union[SchedulerOutput, Tuple]:






        '''simple docstring'''






        _UpperCAmelCase        = self.index_for_timestep(__UpperCamelCase  )

        # advance index counter by 1
        _UpperCAmelCase        = timestep.cpu().item() if torch.is_tensor(__UpperCamelCase  ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
            _UpperCAmelCase        = self.sigmas[step_index]
            _UpperCAmelCase        = self.sigmas[step_index + 1]
        else:
            # 2nd order / Heun's method
            _UpperCAmelCase        = self.sigmas[step_index - 1]
            _UpperCAmelCase        = self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        _UpperCAmelCase        = 0
        _UpperCAmelCase        = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            _UpperCAmelCase        = sigma_hat if self.state_in_first_order else sigma_next
            _UpperCAmelCase        = sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
            _UpperCAmelCase        = sigma_hat if self.state_in_first_order else sigma_next
            _UpperCAmelCase        = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
        elif self.config.prediction_type == "sample":
            _UpperCAmelCase        = model_output
        else:
            raise ValueError(
                f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""  )

        if self.config.clip_sample:
            _UpperCAmelCase        = pred_original_sample.clamp(
                -self.config.clip_sample_range  ,       self.config.clip_sample_range  )

        if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            _UpperCAmelCase        = (sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            _UpperCAmelCase        = sigma_next - sigma_hat

            # store for 2nd order step
            _UpperCAmelCase        = derivative
            _UpperCAmelCase        = dt
            _UpperCAmelCase        = sample
        else:
            # 2. 2nd order / Heun's method
            _UpperCAmelCase        = (sample - pred_original_sample) / sigma_next
            _UpperCAmelCase        = (self.prev_derivative + derivative) / 2

            # 3. take prev timestep & sample
            _UpperCAmelCase        = self.dt
            _UpperCAmelCase        = self.sample

            # free dt and derivative
            # Note, this puts the scheduler in "first order mode"
            _UpperCAmelCase        = None
            _UpperCAmelCase        = None
            _UpperCAmelCase        = None

        _UpperCAmelCase        = sample + derivative * dt

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							torch.FloatTensor  ,       __UpperCamelCase      :							torch.FloatTensor  ,       __UpperCamelCase      :							torch.FloatTensor  ,       )		->torch.FloatTensor:






        '''simple docstring'''






        _UpperCAmelCase        = self.sigmas.to(device=original_samples.device  ,       dtype=original_samples.dtype  )
        if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase  ):
            # mps does not support float64
            _UpperCAmelCase        = self.timesteps.to(original_samples.device  ,       dtype=torch.floataa  )
            _UpperCAmelCase        = timesteps.to(original_samples.device  ,       dtype=torch.floataa  )
        else:
            _UpperCAmelCase        = self.timesteps.to(original_samples.device  )
            _UpperCAmelCase        = timesteps.to(original_samples.device  )

        _UpperCAmelCase        = [self.index_for_timestep(__UpperCamelCase  ,       __UpperCamelCase  ) for t in timesteps]

        _UpperCAmelCase        = sigmas[step_indices].flatten()
        while len(sigma.shape  ) < len(original_samples.shape  ):
            _UpperCAmelCase        = sigma.unsqueeze(-1  )

        _UpperCAmelCase        = original_samples + noise * sigma
        return noisy_samples







    def __len__(      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        return self.config.num_train_timesteps

"""simple docstring"""



def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    if not isinstance(_A       ,   _A   ) or number < 0:
        raise ValueError("""Input must be a non-negative integer"""   )

    _UpperCAmelCase        = 0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
 import doctest

 doctest.testmod()